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Compare ORC and Parquet

Spark/Big Dataeasy0.3 min read

Reviewed by Aditya Kumar · Last reviewed 2026-03-24

**Why format choice matters**: Both columnar; ecosystem and tool support differ. **ORC (Hive)**: Columnar; ACID; predicate pushdown; good compression. Strong in Hive ecosystem. **Parquet**: Columnar; schema evolution; ecosystem standard; Spark, Athena, BigQuery native....

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Frequency

Low

Asked at 1 company

Category

452

questions in Spark/Big Data

Difficulty Split

88E|81M|283H

in this category

Total Bank

1,863

across 7 categories

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Key Concepts Tested

bigqueryspark

Expert Answer

65 words

Why format choice matters: Both columnar; ecosystem and tool support differ. ORC (Hive): Columnar; ACID; predicate pushdown; good compression. Strong in Hive ecosystem. Parquet: Columnar; schema evolution; ecosystem standard; Spark, Athena, BigQuery native. Scalability trade-offs: Parquet = broader connector support; ORC = Hive-native. Cost implications: Similar compression; Parquet often faster in Spark/cloud. Best practice: Parquet for new projects; ORC if Hive-heavy and migration cost prohibitive.

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Pro Tip

Red Flag: Picking based on preference without data. Pro-Move: 'We benchmarked; Parquet 15% faster in our Spark workloads—standardized.'

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According to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 1 company. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.

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